torchsig.models.model_utils.simple_models.simple_convnet_2d

torchsig.models.model_utils.simple_models.simple_convnet_2d(layer_width_list)

takes in a list or tuple of convoluional channel widths and returns a sequential model with those widths used to quickly prototype convolutional neural nets; for example, simple_convnet_2d([3,8,32,64,64,1]) would return a model with 5 convolutional layers that takes in an X by Y image with 3 color channels and outputs an X by Y image with a single channel. Because the returned model doesnt include pooling, striding, or dilation, etc., it does not reduce the scale of the input except possibly in the channel dimension As such, it can take up a great deal of memory, and should not be used by itself to perform complicated tasks on large images